Managing the prediction of metrics in high‐frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high‐frequency limit order markets for mid‐price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of ∼4,000,000 time series samples in total. A day‐based anchored cross‐validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state‐of‐the‐art methodologies. Performance of baseline...
<p>In the dynamic world of financial markets, accurate price predictions are essential for inf...
Over the last three decades, most of the world's stock exchanges have transitioned to electronic tra...
Time-series classification is a complex task filled with noisy data and complexity. Recent studies w...
The increasing complexity of financial trading in recent years revealed the need for methods that ca...
The limit order book of a financial instrument represents its supply and demand at each point in tim...
The vast amount of information characterizing nowadays’s high-frequency financial datasets poses bot...
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) ...
Stock price prediction is a challenging task, but machine learning methods have recently been used s...
Stock price prediction is a challenging task, in which machine learning methods have recently been s...
The field of finance is an interesting field in which much research takes place. In particular, its ...
Time-series forecasting has various applications in a wide range of domains, e.g., forecasting stock...
This thesis investigates the application of machine learning models on foreign exchange data around ...
The stock market moves a large amount of wealth between individuals and institutions daily. Forty mi...
This thesis proposes a convolutional long short-term memory neural network model for predicting limi...
This paper examines, for the first time, the performance of machine learning models in realised vola...
<p>In the dynamic world of financial markets, accurate price predictions are essential for inf...
Over the last three decades, most of the world's stock exchanges have transitioned to electronic tra...
Time-series classification is a complex task filled with noisy data and complexity. Recent studies w...
The increasing complexity of financial trading in recent years revealed the need for methods that ca...
The limit order book of a financial instrument represents its supply and demand at each point in tim...
The vast amount of information characterizing nowadays’s high-frequency financial datasets poses bot...
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) ...
Stock price prediction is a challenging task, but machine learning methods have recently been used s...
Stock price prediction is a challenging task, in which machine learning methods have recently been s...
The field of finance is an interesting field in which much research takes place. In particular, its ...
Time-series forecasting has various applications in a wide range of domains, e.g., forecasting stock...
This thesis investigates the application of machine learning models on foreign exchange data around ...
The stock market moves a large amount of wealth between individuals and institutions daily. Forty mi...
This thesis proposes a convolutional long short-term memory neural network model for predicting limi...
This paper examines, for the first time, the performance of machine learning models in realised vola...
<p>In the dynamic world of financial markets, accurate price predictions are essential for inf...
Over the last three decades, most of the world's stock exchanges have transitioned to electronic tra...
Time-series classification is a complex task filled with noisy data and complexity. Recent studies w...